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Arcade system boards have been using specialized graphics chips since the 1970s. In early video game hardware, the RAM for frame buffers was expensive, so video chips composited data together as the display was being scanned out on the monitor.[6]

In 1985, the Commodore Amiga featured a custom graphics chip, with a blitter unit accelerating bitmap manipulation, line draw, and area fill functions. Also included is a coprocessor (commonly referred to as "The Copper") with its own primitive instruction set, capable of manipulating graphics hardware registers in sync with the video beam (e.g. for per-scanline palette switches, sprite multiplexing, and hardware windowing), or driving the blitter.

In 1987, the IBM 8514 graphics system was released as one of[vague] the first video cards for IBM PC compatibles to implement fixed-function 2D primitives in electronic hardware. The same year, Sharp released the X68000, which used a custom graphics chipset[19] that was powerful for a home computer at the time, with a 65,536 color palette and hardware support for sprites, scrolling and multiple playfields,[20] eventually serving as a development machine for Capcom's CP System arcade board. Fujitsu later competed with the FM Towns computer, released in 1989 with support for a full 16,777,216 color palette.[21]

In 1991, S3 Graphics introduced the S3 86C911, which its designers named after the Porsche 911 as an indication of the performance increase it promised.[24] The 86C911 spawned a host of imitators: by 1995, all major PC graphics chip makers had added 2D acceleration support to their chips.[25][26] By this time, fixed-function Windows accelerators had surpassed expensive general-purpose graphics coprocessors in Windows performance, and these coprocessors faded away from the PC market.

In the PC world, notable failed first tries for low-cost 3D graphics chips were the S3ViRGE, ATIRage, and MatroxMystique. These chips were essentially previous-generation 2D accelerators with 3D features bolted on. Many were even pin-compatible with the earlier-generation chips for ease of implementation and minimal cost. Initially, performance 3D graphics were possible only with discrete boards dedicated to accelerating 3D functions (and lacking 2D GUI acceleration entirely) such as the PowerVR and the 3dfxVoodoo. However, as manufacturing technology continued to progress, video, 2D GUI acceleration and 3D functionality were all integrated into one chip. Rendition'sVerite chipsets were among the first to do this well enough to be worthy of note. In 1997, Rendition went a step further by collaborating with Hercules and Fujitsu on a "Thriller Conspiracy" project which combined a Fujitsu FXG-1 Pinolite geometry processor with a Vérité V2200 core to create a graphics card with a full T&L engine years before Nvidia's GeForce 256. This card, designed to reduce the load placed upon the system's CPU, never made it to market.[citation needed]

OpenGL appeared in the early '90s as a professional graphics API, but originally suffered from performance issues which allowed the Glide API to step in and become a dominant force on the PC in the late '90s.[36] However, these issues were quickly overcome and the Glide API fell by the wayside. Software implementations of OpenGL were common during this time, although the influence of OpenGL eventually led to widespread hardware support. Over time, a parity emerged between features offered in hardware and those offered in OpenGL. DirectX became popular among Windows game developers during the late 90s. Unlike OpenGL, Microsoft insisted on providing strict one-to-one support of hardware. The approach made DirectX less popular as a standalone graphics API initially, since many GPUs provided their own specific features, which existing OpenGL applications were already able to benefit from, leaving DirectX often one generation behind. (See: Comparison of OpenGL and Direct3D.)

Over time, Microsoft began to work more closely with hardware developers, and started to target the releases of DirectX to coincide with those of the supporting graphics hardware. Direct3D 5.0 was the first version of the burgeoning API to gain widespread adoption in the gaming market, and it competed directly with many more-hardware-specific, often proprietary graphics libraries, while OpenGL maintained a strong following. Direct3D 7.0 introduced support for hardware-accelerated transform and lighting (T&L) for Direct3D, while OpenGL had this capability already exposed from its inception. 3D accelerator cards moved beyond being just simple rasterizers to add another significant hardware stage to the 3D rendering pipeline. The NvidiaGeForce 256 (also known as NV10) was the first consumer-level card released on the market with hardware-accelerated T&L, while professional 3D cards already had this capability. Hardware transform and lighting, both already existing features of OpenGL, came to consumer-level hardware in the '90s and set the precedent for later pixel shader and vertex shader units which were far more flexible and programmable.

Nvidia was first to produce a chip capable of programmable shading, the GeForce 3 (code named NV20). Each pixel could now be processed by a short "program" that could include additional image textures as inputs, and each geometric vertex could likewise be processed by a short program before it was projected onto the screen. Used in the Xbox console, it competed with the PlayStation 2 (which used a custom vector DSP for hardware accelerated vertex processing; commonly referred to VU0/VU1). The earliest incarnations of shader execution engines used in Xbox were not general purpose and could not execute arbitrary pixel code. Vertices and pixels were processed by different units which had their own resources with pixel shaders having much tighter constraints (being as they are executed at much higher frequencies than with vertices). Pixel shading engines were actually more akin to a highly customizable function block and didn't really "run" a program. Many of these disparities between vertex and pixel shading wouldn't be addressed until much later with the Unified Shader Model.

By October 2002, with the introduction of the ATIRadeon 9700 (also known as R300), the world's first Direct3D 9.0 accelerator, pixel and vertex shaders could implement looping and lengthy floating point math, and were quickly becoming as flexible as CPUs, yet orders of magnitude faster for image-array operations. Pixel shading is often used for bump mapping, which adds texture, to make an object look shiny, dull, rough, or even round or extruded.[37]

With the introduction of the GeForce 8 series, which was produced by Nvidia, and then new generic stream processing unit GPUs became a more generalized computing device. Today, parallel GPUs have begun making computational inroads against the CPU, and a subfield of research, dubbed GPU Computing or GPGPU for General Purpose Computing on GPU, has found its way into fields as diverse as machine learning,[38]oil exploration, scientific image processing, linear algebra,[39]statistics,[40] 3D reconstruction and even stock options pricing determination. GPGPU at the time was the precursor to what we now call compute shaders (e.g. CUDA, OpenCL, DirectCompute) and actually abused the hardware to a degree by treating the data passed to algorithms as texture maps and executing algorithms by drawing a triangle or quad with an appropriate pixel shader. This obviously entails some overheads since we involve units like the Scan Converter where they aren't really needed (nor do we even care about the triangles, except to invoke the pixel shader). Over the years, the energy consumption of GPUs has increased and to manage it, several techniques have been proposed.[41]

Nvidia's CUDA platform, first introduced in 2007,[42] was the earliest widely adopted programming model for GPU computing. More recently OpenCL has become broadly supported. OpenCL is an open standard defined by the Khronos Group which allows for the development of code for both GPUs and CPUs with an emphasis on portability.[43] OpenCL solutions are supported by Intel, AMD, Nvidia, and ARM, and according to a recent report by Evan's Data, OpenCL is the GPGPU development platform most widely used by developers in both the US and Asia Pacific.[citation needed]

In 2010, Nvidia began a partnership with Audi to power their cars' dashboards. These Tegra GPUs were powering the cars' dashboard, offering increased functionality to cars' navigation and entertainment systems.[44] Advancements in GPU technology in cars has helped push self-driving technology.[45] AMD's Radeon HD 6000 Series cards were released in 2010 and in 2011, AMD released their 6000M Series discrete GPUs to be used in mobile devices.[46] The Kepler line of graphics cards by Nvidia came out in 2012 and were used in the Nvidia's 600 and 700 series cards. A new feature in this new GPU microarchitecture included GPU boost, a technology adjusts the clock-speed of a video card to increase or decrease it according to its power draw.[47] The Kepler microarchitecture was manufactured on the 28 nm process.

The PS4 and Xbox One were released in 2013, they both use GPUs based on AMD's Radeon HD 7850 and 7790. Nvidia's Kepler line of GPUs was followed by the Maxwell line, manufactured on the same process. 28 nm chips by Nvidia were manufactured by TSMC, the Taiwan Semiconductor Manufacturing Company, that was manufacturing using the 28 nm process at the time. Compared to the 40 nm technology from the past, this new manufacturing process allowed a 20 percent boost in performance while drawing less power.[48][49]Virtual realityheadsets like the Oculus Rift and the HTC Vive have very high system requirements. VR headset manufacturers recommended the GTX 970 and the R9 290X or better at the time of their release.[50][51]Pascal is the newest generation of consumer graphics cards by Nvidia released in 2016. The GeForce 10 series of cards are under this generation of graphics cards. They are made using the 16 nm manufacturing process which improves upon previous microarchitectures.[52] However, as of 25 February 2018, Nvidia has released one non-consumer card under the new Volta architecture, the Titan V. Changes from the Titan XP, Pascal's high-end card, include an increase in the number of CUDA cores, the addition of tensor cores, and high-bandwidth memory. Tensor cores are cores specially designed for deep learning, while high-bandwidth memory is on-die, stacked, lower-clocked memory that offers an extremely wide memory bus that is useful for the Titan V's intended purpose. To emphasize that the Titan V is not a gaming card, Nvidia removed the "Geforce GTX" suffix it adds to consumer gaming cards. Polaris 11 and Polaris 10 GPUs from AMD are fabricated a 14-nanometer process. Their release results in a substantial increase in the performance per watt of AMD video cards.[53] AMD has also released for its high-end market the Vega GPUs, which also feature high-bandwidth memory like the Titan V. However, it should be noted that performance is limited because AMD's current driver for Vega cards limits the fan to 40%, leading to thermal throttling.[54]

With the emergence of deep learning, the importance of GPUs has increased. In research done by Indigo, it was found that while training deep learning neural networks, GPUs can be 250 times faster than CPUs. The explosive growth of Deep Learning in recent years has been attributed to the emergence of general purpose GPUs.[citation needed] There has been some level of competition in this area with ASICs, most prominently the Tensor Processing Unit (TPU) made by Google. However, these can require changes to existing code and GPUs are still very popular.

The ATI HD5470 GPU (above) features UVD 2.1 which enables it to decode AVC and VC-1 video formats

Most GPUs made since 1995 support the YUVcolor space and hardware overlays, important for digital video playback, and many GPUs made since 2000 also support MPEG primitives such as motion compensation and iDCT. This process of hardware accelerated video decoding, where portions of the video decoding process and video post-processing are offloaded to the GPU hardware, is commonly referred to as "GPU accelerated video decoding", "GPU assisted video decoding", "GPU hardware accelerated video decoding" or "GPU hardware assisted video decoding".

A dedicated GPU is not necessarily removable, nor does it necessarily interface with the motherboard in a standard fashion. The term "dedicated" refers to the fact that dedicated graphics cards have RAM that is dedicated to the card's use, not to the fact that most dedicated GPUs are removable. Further, this RAM is usually specially selected for the expected serial workload of the graphics card (see GDDR). Dedicated GPUs for portable computers are most commonly interfaced through a non-standard and often proprietary slot due to size and weight constraints. Such ports may still be considered PCIe or AGP in terms of their logical host interface, even if they are not physically interchangeable with their counterparts.

Technologies such as SLI by Nvidia and CrossFire by AMD allow multiple GPUs to draw images simultaneously for a single screen, increasing the processing power available for graphics.

The position of an integrated GPU in a northbridge/southbridge system layout

An ASRock motherboard with integrated graphics, which has HDMI, VGA and DVI outs.

Integrated graphics, shared graphics solutions, integrated graphics processors (IGP) or unified memory architecture (UMA) utilize a portion of a computer's system RAM rather than dedicated graphics memory. IGPs can be integrated onto the motherboard as part of the chipset, or on the same die with the CPU (like AMD APU or Intel HD Graphics). On certain motherboards [59] AMD's IGPs can use dedicated sideport memory. This is a separate fixed block of high performance memory that is dedicated for use by the GPU. In early 2007, computers with integrated graphics account for about 90% of all PC shipments.[60][needs update] They are less costly to implement than dedicated graphics processing, but tend to be less capable. Historically, integrated processing was often considered unfit to play 3D games or run graphically intensive programs but could run less intensive programs such as Adobe Flash. Examples of such IGPs would be offerings from SiS and VIA circa 2004.[61] However, modern integrated graphics processors such as AMD Accelerated Processing Unit and Intel HD Graphics are more than capable of handling 2D graphics or low stress 3D graphics.

As a GPU is extremely memory intensive, integrated processing may find itself competing with the CPU for the relatively slow system RAM, as it has minimal or no dedicated video memory. IGPs can have up to 29.856 GB/s of memory bandwidth from system RAM, whereas a graphics card may have up to 264 GB/s of bandwidth between its RAM and GPU core. This memory bus bandwidth can limit the performance of the GPU.[citation needed] Older integrated graphics chipsets lacked hardware transform and lighting, but newer ones include it.[62][63]

This newer class of GPUs competes with integrated graphics in the low-end desktop and notebook markets. The most common implementations of this are ATI's HyperMemory and Nvidia's TurboCache.

Hybrid graphics cards are somewhat more expensive than integrated graphics, but much less expensive than dedicated graphics cards. These share memory with the system and have a small dedicated memory cache, to make up for the high latency of the system RAM. Technologies within PCI Express can make this possible. While these solutions are sometimes advertised as having as much as 768MB of RAM, this refers to how much can be shared with the system memory.

It is becoming increasingly common to use a general purpose graphics processing unit (GPGPU) as a modified form of stream processor (or a vector processor), running compute kernels. This concept turns the massive computational power of a modern graphics accelerator's shader pipeline into general-purpose computing power, as opposed to being hard wired solely to do graphical operations. In certain applications requiring massive vector operations, this can yield several orders of magnitude higher performance than a conventional CPU. The two largest discrete (see "Dedicated graphics cards" above) GPU designers, AMD and Nvidia, are beginning to pursue this approach with an array of applications. Both Nvidia and AMD have teamed with Stanford University to create a GPU-based client for the Folding@home distributed computing project, for protein folding calculations. In certain circumstances the GPU calculates forty times faster than the conventional CPUs traditionally used by such applications.[64][65]

Furthermore, GPU-based high performance computers are starting to play a significant role in large-scale modelling. Three of the 10 most powerful supercomputers in the world take advantage of GPU acceleration.[66]

GPU supports API extensions to the C programming language such as OpenCL and OpenMP. Furthermore, each GPU vendor introduced its own API which only works with their cards, AMD APP SDK and CUDA from AMD and Nvidia, respectively. These technologies allow specified functions called compute kernels from a normal C program to run on the GPU's stream processors. This makes it possible for C programs to take advantage of a GPU's ability to operate on large buffers in parallel, while still using the CPU when appropriate. CUDA is also the first API to allow CPU-based applications to directly access the resources of a GPU for more general purpose computing without the limitations of using a graphics API.[citation needed]

Since 2005 there has been interest in using the performance offered by GPUs for evolutionary computation in general, and for accelerating the fitness evaluation in genetic programming in particular. Most approaches compile linear or tree programs on the host PC and transfer the executable to the GPU to be run. Typically the performance advantage is only obtained by running the single active program simultaneously on many example problems in parallel, using the GPU's SIMD architecture.[67][68] However, substantial acceleration can also be obtained by not compiling the programs, and instead transferring them to the GPU, to be interpreted there.[69][70] Acceleration can then be obtained by either interpreting multiple programs simultaneously, simultaneously running multiple example problems, or combinations of both. A modern GPU can readily simultaneously interpret hundreds of thousands of very small programs.

An external GPU is a graphics processor located outside of the housing of the computer. External graphics processors are sometimes used with laptop computers. Laptops might have a substantial amount of RAM and a sufficiently powerful central processing unit (CPU), but often lack a powerful graphics processor, and instead have a less powerful but more energy-efficient on-board graphics chip. On-board graphics chips are often not powerful enough for playing the latest games, or for other graphically intensive tasks, such as editing video.

Therefore, it is desirable to be able to attach a GPU to some external bus of a notebook. PCI Express is the only bus commonly used for this purpose. The port may be, for example, an ExpressCard or mPCIe port (PCIe ×1, up to 5 or 2.5 Gbit/s respectively) or a Thunderbolt 1, 2, or 3 port (PCIe ×4, up to 10, 20, or 40 Gbit/s respectively). Those ports are only available on certain notebook systems.[71][72]

External GPUs have had little official vendor support. However this has not stopped enthusiasts from implementing eGPU setups.[73][74]